The present invention relates to the field of data analysis and, more particularly, to an intelligent product feedback analytics tool.
The Internet contains an overwhelming amount of information. In particular, the Internet has allowed for the collecting and sharing of reviews and feedback about a wide variety of products and services from actual product users. Such information allows potential buyers of a product or service to evaluate others' experiences prior to purchase and provide insight on the products and/or services that they use.
Unfortunately, the first problem for a user attempting to research a product or service is the sheer amount of product feedback information returned by search engines. The user is forced to ascertain the usefulness and validity of this plethora of information, which is another daunting and time-consuming task. For example, the user may have to do further research to determine any biases of a third-party review.
Typically, product feedback information includes a rating scale for the product or service, which poses another problem to the user. Not all rating scales used by different feedback sources are the same. The user is left to figure out how the four-star-based rating system used by the product's manufacturer's Web site equivocates to the five-star-based rating system used by an independent review Web site. Again, this is an arduous and time-consuming process.
However, even after investing all this time and energy, it is still easy for the user to select the wrong product or service to fit their need. This selection problem stems from a disconnect between the provided rating and the reviewer's actual feedback (i.e., the included text explaining their rating of the product). That is, a reviewer could give a product a “poor” or “bad” product rating (e.g., one star out of five) because they are trying to use the product outside the purview of its intended use or because they are including non-product criteria, such as delivery or shipping problems, in the rating.